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An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering

  • * Corresponding author: Tie Zhang

    * Corresponding author: Tie Zhang
This work is supported by the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds, No. 2013ZCX02
Abstract / Introduction Full Text(HTML) Figure(3) / Table(3) Related Papers Cited by
  • Fuzzy time series shows great advantages in dealing with incomplete or unreasonable data. But most of them are based on fuzzy AR time series model, so it is necessary to add MA variables to the fuzzy time series [10] to make it more accurate. An improved ARMA(1, 1) type fuzzy time series based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables was proposed in this paper. To take full account of the errors, the prediction errors were added to the forecast fuzzy sets, and it made the first-order fuzzy logical relationship sets more exact. In order to verify the advantage of the proposed method, it was applied to predict the stock prices of State Bank of India (SBI) and the packet disordering from a common source host in the Northeast University to www. yahoo. com. The experimental results showed that the proposed model was more precise than other models.

    Mathematics Subject Classification: 62A86.

    Citation:

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  • Figure 1.  A comparison between the proposed model and other models

    Figure 2.  A comparison between the proposed model and other models

    Figure 3.  A comparison between the delays of the proposed model and the real delays

    Table 1.  Example of weights.

    Time FLR Weight
    $ \; t=1 $ $ A_i \rightarrow A_j $ 1
    $ \; t=2 $ $ A_i \rightarrow A_k $ 1
    $ \; t=3 $ $ A_i \rightarrow A_j $ 1
    $ \; t=4 $ $ A_i \rightarrow A_l $ 1
    $ \; t=5 $ $ A_i \rightarrow A_j $ 1
     | Show Table
    DownLoad: CSV

    Table 2.  The predication values.

    Year Actual price The rate of change PRC Predicted
    $ t $ (in rupee) $ r(t)\% $ values
    6/5/2012 2080.25
    6/6/2012 2159.45 3.81 3.81
    6/7/2012 2167.85 0.39 0.38 2167.66
    6/8/2012 2180.05 0.56 0.61 2181.07
    6/11/2012 2164.55 -0.71 -0.65 2165.88
    6/12/2012 2206.90 1.96 1.91 2205.89
    6/13/2012 2222.25 0.70 0.68 2221.91
    6/14/2012 2154.25 -3.06 -3.12 2152.92
    6/15/2012 2182.80 1.33 0.99 2175.58
    6/18/2012 2087.65 -4.36 -4.36 2087.63
    7/17/2012 2198.85 0.20 0.16 2197.96
    7/18/2012 2185.95 -0.57 -0.65 2184.56
    7/19/2012 2157.75 -1.29 -1.29 2157.75
    7/20/2012 2134.55 -1.08 -1.03 2135.53
    7/23/2012 2092.55 -1.97 -1.96 2092.71
    7/24/2012 2094.80 0.11 0.09 2094.43
    7/25/2012 2070.65 -1.15 -1.03 2073.22
    7/26/2012 2017.15 -2.58 -2.40 2020.95
    7/27/2012 1941.20 -3.77 -3.74 1941.71
    7/31/2012 2005.20 3.30 3.36 2006.42
     | Show Table
    DownLoad: CSV

    Table 3.  MSE of different methods.

    Errors [4] [18] [16] Proposed model
    RMSE 54.1958 82.3197 32.2586 1.6498
     | Show Table
    DownLoad: CSV
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